arXiv Machine Learning By Qi Li, Wendong Huang, Qichen Ye, Wutong Xu, Cheems Wang, Wei Yuan, Miao Xu, Zhiyu Mou, Guan Wang, Rongquan Bai, Chuan Yu, Jian Xu

HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Bidding Environments

Read the original on arXiv Machine Learning →

arXiv:2510. 15238v2 Announce Type: replace-cross Abstract: Optimizing a single advertising campaign across heterogeneous channels is a central challenge in industrial autobidding.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jul 22

JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing

arXiv:2604. 05845v2 Announce Type: replace-cross Abstract: Auto-bidding services optimize real-time bidding strategies for advertisers under key performance indicator (KPI) constraints such as target return on investment and budget.

By Linghui Meng, Chun Gan, Shengsheng Niu, Chengcheng Zhang, Chenchen Li, Chuan Yang, Yi Mao, Xin Zhu, Jie He, Zhangang Lin, Ching Law
arXiv Machine Learning
Jul 31

PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective

arXiv:2607. 27265v1 Announce Type: new Abstract: Real-time bidding is central to computational advertising, comprising three elements: Supply Side Platform (SSP) selling ad impressions, Demand Side Platform (DSP) bidding for advertisers, and Ad Exchange conducting auctions between them.

By Shengtian Yang, Yewen Li, Peng Jiang, Zhiyi Lyu, Bo An, Peng Jiang, Qingpeng Cai, Lei Feng